Mining temporal patterns without predefined time windows

This paper proposes algorithms for discovering temporal patterns without predefined time windows. The problem of discovering temporal patterns is divided into two sub-tasks: (1) using "cheap statistics" for dependence testing and candidates removal, (2) identifying the temporal relationships between dependent event types. The dependence problem is formulated as the problem of comparing two probability distributions and is solved using a technique reminiscent of the distance methods used in spatial point process, while the latter problem is solved using an approach based on chi-squared tests. Experiments are conducted to evaluate the effectiveness and scalability of the proposed methods.